Probabilistic Slow Features for Behavior Analysis

Lazaros Zafeiriou, Mihalis A. Nicolaou, Stefanos Zafeiriou, Symeon Nikitidis, Maja Pantic

    Research output: Contribution to journalArticleAcademicpeer-review

    14 Citations (Scopus)


    A recently introduced latent feature learning technique for time-varying dynamic phenomena analysis is the so-called slow feature analysis (SFA). SFA is a deterministic component analysis technique for multidimensional sequences that, by minimizing the variance of the first-order time derivative approximation of the latent variables, finds uncorrelated projections that extract slowly varying features ordered by their temporal consistency and constancy. In this paper, we propose a number of extensions in both the deterministic and the probabilistic SFA optimization frameworks. In particular, we derive a novel deterministic SFA algorithm that is able to identify linear projections that extract the common slowest varying features of two or more sequences. In addition, we propose an expectation maximization (EM) algorithm to perform inference in a probabilistic formulation of SFA and similarly extend it in order to handle two and more time-varying data sequences. Moreover, we demonstrate that the probabilistic SFA (EM-SFA) algorithm that discovers the common slowest varying latent space of multiple sequences can be combined with dynamic time warping techniques for robust sequence time-alignment. The proposed SFA algorithms were applied for facial behavior analysis, demonstrating their usefulness and appropriateness for this task. © 2015 IEEE.
    Original languageEnglish
    Pages (from-to)1034-1048
    Number of pages15
    JournalIEEE transactions on neural networks and learning systems
    Issue number5
    Publication statusPublished - May 2016


    • EC Grant Agreement nr.: FP7/645094
    • HMI-HF: Human Factors
    • Temporal alignment
    • IR-103790
    • linear dynamical system (LDS)
    • slow feature analysis (SFA)
    • Behavior analysis
    • EWI-27125
    • n/a OA procedure


    Dive into the research topics of 'Probabilistic Slow Features for Behavior Analysis'. Together they form a unique fingerprint.

    Cite this